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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and

Mahdi Maktab Dar Oghaz¹, Manzoor Razaak², Paolo Remagnino³

¹Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK.

Sensors (Basel, Switzerland)

|June 24, 2022

Summary

This study introduces a new Convolutional Neural Network (CNN) model to improve small object detection in aerial imagery. The enhanced model significantly boosts performance in identifying small objects from drone-based images.

Keywords:

SSD deconvolution feature fusion small object detection super-resolution

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Area of Science:

Computer Vision
Artificial Intelligence
Remote Sensing

Background:

Small object detection in aerial imagery is challenging due to high altitudes and wide-angle lenses.
General object detectors struggle with small objects, losing spatial features and feature representation.
Imbalance between small objects and background further complicates detection.

Purpose of the Study:

To address the limitations of current object detectors for small objects in aerial images.
To propose a novel Convolutional Neural Network (CNN) model for enhanced small object detection.
To improve feature representation of small objects at the prediction layer.

Main Methods:

Utilized the Single Shot Multi-box Detector (SSD) as the baseline network.
Integrated feature enhancement modules: super-resolution, deconvolution, and feature fusion.
Evaluated the model on three datasets, including two aerial image datasets with predominantly small objects.

Main Results:

The proposed CNN model demonstrated improved mean Average Precision (mAP) and Recall.
Achieved superior performance compared to state-of-the-art small object detectors.
Effectively enhanced feature representation for small objects.

Conclusions:

The developed CNN model offers a significant advancement in small object detection for aerial imagery.
The feature enhancement modules are crucial for improving detection accuracy of small objects.
The model shows promise for applications requiring precise identification of small objects in remote sensing data.